@phdthesis{22255, abstract = {This thesis studies spectral rigidity and nonrigidity phenomena in dynamical systems. The central question is whether a dynamical system can be determined, up to a natural conjugacy, from its spectrum. We consider three related spectra: the length spectrum, the action spectrum, and the Lyapunov spectrum. The first part of the thesis concerns Liouville metrics on the two-dimensional torus. It is a long-standing folklore conjecture that Liouville metrics are the only integrable metrics on the torus. We prove a length-spectral rigidity result for linear conformal deformations of Liouville metrics by exploiting the dynamical properties of the rational tori -- analogues of the resonant convex caustics in billiards. We also establish a complementary classification result showing that marked-length-isospectral Liouville metrics are characterized by rearrangements of the one-dimensional functions appearing in their conformal factors, generalizing a theorem of Abbondandolo-Mazzucchelli. In particular, the second result gives nonrigidity examples within the class of Liouville metrics. The second part of the thesis studies the standard map from the viewpoint of action and Lyapunov spectra. We construct nontrivial deformations of the standard map which preserve the symplectic actions (respectively, the Lyapunov exponents) of infinitely many periodic orbits accumulating on an invariant curve. The proof combines a resonant normal form construction with Picard iteration schemes to obtain a sequence of periodic orbits accumulating on an invariant curve with a Liouville rotation number. Within the resonant normal forms we capture the dependence of these periodic orbits on the resonant Fourier coefficients of the dynamics on the invariant curve and, using the contraction mapping principle, obtain a suitable deformation achieving the prescribed spectral data associated with this sequence of orbits. The result can be viewed as a symplectic twist-map analogue of a length-spectral nonrigidity phenomenon for Riemannian manifolds and convex billiards, and it motivates the existence problem for similar 'partially length-isospectral' deformations of strictly convex billiard tables. }, author = {Li, Yunzhe}, issn = {2663-337X}, pages = {131}, publisher = {Institute of Science and Technology Austria}, title = {{Spectral rigidity and nonrigidity of dynamical systems}}, doi = {10.15479/AT-ISTA-22255}, year = {2026}, } @misc{18697, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.}, author = {Danzl, Johann G and Lyudchik, Julia and Kreuzinger, Caroline}, publisher = {Institute of Science and Technology Austria}, title = {{Light-microscopy based connectomic reconstruction of mammalian brain tissue}}, doi = {10.15479/AT:ISTA:18697}, year = {2025}, } @article{19003, abstract = {Super-resolution methods provide far better spatial resolution than the optical diffraction limit of about half the wavelength of light (∼200-300 nm). Nevertheless, they have yet to attain widespread use in plants, largely due to plants’ challenging optical properties. Expansion microscopy improves effective resolution by isotropically increasing the physical distances between sample structures while preserving relative spatial arrangements and clearing the sample. However, its application to plants has been hindered by the rigid, mechanically cohesive structure of plant tissues. Here, we report on whole-mount expansion microscopy of thale cress (Arabidopsis thaliana) root tissues (PlantEx), achieving a four-fold resolution increase over conventional microscopy. Our results highlight the microtubule cytoskeleton organization and interaction between molecularly defined cellular constituents. Combining PlantEx with stimulated emission depletion (STED) microscopy, we increase nanoscale resolution and visualize the complex organization of subcellular organelles from intact tissues by example of the densely packed COPI-coated vesicles associated with the Golgi apparatus and put these into a cellular structural context. Our results show that expansion microscopy can be applied to increase effective imaging resolution in Arabidopsis root specimens. }, author = {Gallei, Michelle C and Truckenbrodt, Sven M and Kreuzinger, Caroline and Inumella, Syamala and Vistunou, Vitali and Sommer, Christoph M and Tavakoli, Mojtaba and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Jahr, Wiebke and Randuch, Marek and Johnson, Alexander J and Benková, Eva and Friml, Jiří and Danzl, Johann G}, issn = {1532-298X}, journal = {The Plant Cell}, number = {4}, publisher = {Oxford University Press}, title = {{Super-resolution expansion microscopy in plant roots}}, doi = {10.1093/plcell/koaf006}, volume = {37}, year = {2025}, } @phdthesis{19048, abstract = {Rotations are found in physics problems at all scales: from spatial motion of celestial bodies, to transitions between quantum states of atoms and molecules. Mathematically, they represent a fundamental class of transformations and symmetries. Unlike spatial displacements, rotational transformations in three-dimensional space are non-commutative: the result of applying a sequence of rotations depends on the order of these operations. This feature makes the emergent physics that involves rotations rather intricate, but instrumental for studies of highly-interconnected many-body systems. In the presence of an environment, rotational properties of an object change, due to the interaction with particles of the environment. Owing to the complexity of this interaction, it can be engineered to exhibit certain properties of interest. In this Thesis, we examine several scenarios of how the rotational behavior of an impurity can be modified by interactions with its environment.}, author = {Maslov, Mikhail}, issn = {2663-337X}, pages = {86}, publisher = {Institute of Science and Technology Austria}, title = {{Emergent physics of rotating quantum impurities in many-body environments}}, doi = {10.15479/at:ista:19048}, year = {2025}, } @article{19704, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.}, author = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G}, issn = {1476-4687}, journal = {Nature}, pages = {398--410}, publisher = {Springer Nature}, title = {{Light-microscopy-based connectomic reconstruction of mammalian brain tissue}}, doi = {10.1038/s41586-025-08985-1}, volume = {642}, year = {2025}, } @article{20080, abstract = {Introduction: Acid-growth theory has been postulated in the 70s to explain the rapid elongation of plant cells in response to the hormone auxin. More recently, it has been demonstrated that activation of the proton ATPs pump (H+-ATPs) promoting acidification of the apoplast is the principal mechanism by which auxin and other hormones such as brassinosteroids (BR) induce cell elongation. Despite these advances, the impact of this acidification on the mechanical properties of the cell wall remained largely unexplored. Methods: Here, we use elongation assays of Arabidopsis thaliana hypocotyls and Atomic Force Microscopy (AFM) to correlate hormone-induced tissue elongation and local changes in cell wall mechanical properties. Furthermore, employing transgenic lines over-expressing Pectin Methyl Esterase (PME), along with calcium chelators, we investigate the effect of pectin modification in hormone-driven cell elongation. Results: We demonstrate that acidification of apoplast is necessary and sufficient to induce cell elongation through promoting cell wall softening. Moreover, we show that enhanced PME activity can induce both cell wall softening or stiffening in extracellular calcium dependent-manner and that tight control of PME activity is required for proper hypocotyl elongation. Discussion: Our results confirm a dual role of PME in plant cell elongation. However, further investigation is needed to assess the status of pectin following short- or long-term PME treatments in order to determine if pectin methyl-esterification might promote its degradation as well as the role of PME inhibitors upon PME induction.}, author = {Gallemi, Marçal and Montesinos López, Juan C and Zarevski, Nikola and Pribyl, Jan and Skládal, Petr and Hannezo, Edouard B and Benková, Eva}, issn = {1664-462X}, journal = {Frontiers in Plant Science}, publisher = {Frontiers Media}, title = {{Dual role of pectin methyl esterase activity in the regulation of plant cell wall biophysical properties}}, doi = {10.3389/fpls.2025.1612366}, volume = {16}, year = {2025}, } @phdthesis{20357, author = {Ruzickova, Natalia}, isbn = {978-3-99078-066-4}, issn = {2663-337X}, keywords = {gene regulation, networks, omnigenic model, pancreas, collective behaviour}, pages = {160}, publisher = {Institute of Science and Technology Austria}, title = {{Effect propagation in biological networks}}, doi = {10.15479/AT-ISTA-20357}, year = {2025}, } @article{14257, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.}, author = {Michalska, Julia M and Lyudchik, Julia and Velicky, Philipp and Korinkova, Hana and Watson, Jake and Cenameri, Alban and Sommer, Christoph M and Amberg, Nicole and Venturino, Alessandro and Roessler, Karl and Czech, Thomas and Höftberger, Romana and Siegert, Sandra and Novarino, Gaia and Jonas, Peter M and Danzl, Johann G}, issn = {1546-1696}, journal = {Nature Biotechnology}, pages = {1051--1064}, publisher = {Springer Nature}, title = {{Imaging brain tissue architecture across millimeter to nanometer scales}}, doi = {10.1038/s41587-023-01911-8}, volume = {42}, year = {2024}, } @unpublished{18677, abstract = {The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.}, author = {Tavakoli, Mojtaba and Lyudchik, Julia and Januszewski, Michał and Vistunou, Vitali and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Sommer, Christoph M and Kreuzinger, Caroline and Oliveira, Bárbara and Cenameri, Alban and Novarino, Gaia and Jain, Viren and Danzl, Johann G}, booktitle = {bioRxiv}, title = {{Light-microscopy based dense connectomic reconstruction of mammalian brain tissue}}, doi = {10.1101/2024.03.01.582884}, year = {2024}, } @unpublished{18689, abstract = {Multiplexed fluorescence microscopy imaging is widely used in biomedical applications. However, simultaneous imaging of multiple fluorophores can result in spectral leaks and overlapping, which greatly degrades image quality and subsequent analysis. Existing popular spectral unmixing methods are mainly based on computational intensive linear models and the performance is heavily dependent on the reference spectra, which may greatly preclude its further applications. In this paper, we propose a deep learning-based blindly spectral unmixing method, termed AutoUnmix, to imitate the physical spectral mixing process. A tranfer learning framework is further devised to allow our AutoUnmix adapting to a variety of imaging systems without retraining the network. Our proposed method has demonstrated real-time unmixing capabilities, surpassing existing methods by up to 100-fold in terms of unmixing speed. We further validate the reconstruction performance on both synthetic datasets and biological samples. The unmixing results of AutoUnmix achieve a highest SSIM of 0.99 in both three- and four-color imaging, with nearly up to 20% higher than other popular unmixing methods. Due to the desirable property of data independency and superior blind unmixing performance, we believe AutoUnmix is a powerful tool to study the interaction process of different organelles labeled by multiple fluorophores.}, author = {Gallei, Michelle C and Truckenbrodt, Sven M and Kreuzinger, Caroline and Inumella, Syamala and Vistunou, Vitali and Sommer, Christoph M and Tavakoli, Mojtaba and Agudelo Duenas, Nathalie and Vorlaufer, Jakob and Jahr, Wiebke and Randuch, Marek and Johnson, Alexander J and Benková, Eva and Friml, Jiří and Danzl, Johann G}, booktitle = {bioRxiv}, title = {{Super-resolution expansion microscopy in plant roots}}, doi = {10.1101/2024.02.21.581330}, year = {2024}, } @phdthesis{18574, abstract = {Biological vision is unlike a camera; rather than transmitting light information faithfully, early visual circuits process the visual scene to convey only the relevant information in an efficient manner. Consequentially, the nature of this visual processing then depends on what is the relevant information in a scene and on the notion of efficiency. In this work, I study how visual processing is modulated by two different variations in the visual scene. First, I discovered that in the mouse (Mus musculus) retina, Retinal Ganglion Cells in the upper and lower visual field have differences in the center surround structure of their receptive fields. Comparison with models of efficient coding show that this adaptation likely evolved to cope with the brightness gradient from the sky to the ground that is pervasive in natural scenes. In the second project, I study how the downstream neurons in the Superior Colliculus dynamically change their temporal selectivity depending on the ambient luminance and behavioral state. As the scene gets darker or when the animal is is less aroused, the neuronal responses get laggier, while still maintaining their relative timing with respect to the population. Overall, this work emphasises the need to understand visual processing in the context of specific demands of the animal in its the environment. The adaptive changes in the visual system, from the retinal ganglion cells to the superior colliculus, highlight the intricate ways in which biological vision optimizes the processing of visual information. }, author = {Gupta, Divyansh}, isbn = {978-3-99078-050-3}, issn = {2663-337X}, pages = {86}, publisher = {Institute of Science and Technology Austria}, title = {{Visual adaptations to natural statistics}}, doi = {10.15479/at:ista:18574}, year = {2024}, } @misc{12817, abstract = {3D-reconstruction of living brain tissue down to individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain’s complex and dense information processing network. However, it has been hindered by insufficient 3D-resolution, inadequate signal-to-noise-ratio, and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine learning technology, LIONESS (Live Information-Optimized Nanoscopy Enabling Saturated Segmentation). It leverages optical modifications to stimulated emission depletion (STED) microscopy in comprehensively, extracellularly labelled tissue and prior information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise-ratio, and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D-reconstruction at synapse level incorporating molecular, activity, and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.}, author = {Danzl, Johann G}, publisher = {Institute of Science and Technology Austria}, title = {{Research data for the publication "Dense 4D nanoscale reconstruction of living brain tissue"}}, doi = {10.15479/AT:ISTA:12817}, year = {2023}, } @misc{13126, abstract = {Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here, we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.}, author = {Danzl, Johann G}, publisher = {Institute of Science and Technology Austria}, title = {{Research data for the publication "Imaging brain tissue architecture across millimeter to nanometer scales"}}, doi = {10.15479/AT:ISTA:13126}, year = {2023}, } @article{13267, abstract = {Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.}, author = {Velicky, Philipp and Miguel Villalba, Eder and Michalska, Julia M and Lyudchik, Julia and Wei, Donglai and Lin, Zudi and Watson, Jake and Troidl, Jakob and Beyer, Johanna and Ben Simon, Yoav and Sommer, Christoph M and Jahr, Wiebke and Cenameri, Alban and Broichhagen, Johannes and Grant, Seth G.N. and Jonas, Peter M and Novarino, Gaia and Pfister, Hanspeter and Bickel, Bernd and Danzl, Johann G}, issn = {1548-7105}, journal = {Nature Methods}, pages = {1256--1265}, publisher = {Springer Nature}, title = {{Dense 4D nanoscale reconstruction of living brain tissue}}, doi = {10.1038/s41592-023-01936-6}, volume = {20}, year = {2023}, } @phdthesis{9418, abstract = {Deep learning is best known for its empirical success across a wide range of applications spanning computer vision, natural language processing and speech. Of equal significance, though perhaps less known, are its ramifications for learning theory: deep networks have been observed to perform surprisingly well in the high-capacity regime, aka the overfitting or underspecified regime. Classically, this regime on the far right of the bias-variance curve is associated with poor generalisation; however, recent experiments with deep networks challenge this view. This thesis is devoted to investigating various aspects of underspecification in deep learning. First, we argue that deep learning models are underspecified on two levels: a) any given training dataset can be fit by many different functions, and b) any given function can be expressed by many different parameter configurations. We refer to the second kind of underspecification as parameterisation redundancy and we precisely characterise its extent. Second, we characterise the implicit criteria (the inductive bias) that guide learning in the underspecified regime. Specifically, we consider a nonlinear but tractable classification setting, and show that given the choice, neural networks learn classifiers with a large margin. Third, we consider learning scenarios where the inductive bias is not by itself sufficient to deal with underspecification. We then study different ways of ‘tightening the specification’: i) In the setting of representation learning with variational autoencoders, we propose a hand- crafted regulariser based on mutual information. ii) In the setting of binary classification, we consider soft-label (real-valued) supervision. We derive a generalisation bound for linear networks supervised in this way and verify that soft labels facilitate fast learning. Finally, we explore an application of soft-label supervision to the training of multi-exit models.}, author = {Bui Thi Mai, Phuong}, issn = {2663-337X}, pages = {125}, publisher = {Institute of Science and Technology Austria}, title = {{Underspecification in deep learning}}, doi = {10.15479/AT:ISTA:9418}, year = {2021}, } @phdthesis{8032, abstract = {Algorithms in computational 3-manifold topology typically take a triangulation as an input and return topological information about the underlying 3-manifold. However, extracting the desired information from a triangulation (e.g., evaluating an invariant) is often computationally very expensive. In recent years this complexity barrier has been successfully tackled in some cases by importing ideas from the theory of parameterized algorithms into the realm of 3-manifolds. Various computationally hard problems were shown to be efficiently solvable for input triangulations that are sufficiently “tree-like.” In this thesis we focus on the key combinatorial parameter in the above context: we consider the treewidth of a compact, orientable 3-manifold, i.e., the smallest treewidth of the dual graph of any triangulation thereof. By building on the work of Scharlemann–Thompson and Scharlemann–Schultens–Saito on generalized Heegaard splittings, and on the work of Jaco–Rubinstein on layered triangulations, we establish quantitative relations between the treewidth and classical topological invariants of a 3-manifold. In particular, among other results, we show that the treewidth of a closed, orientable, irreducible, non-Haken 3-manifold is always within a constant factor of its Heegaard genus.}, author = {Huszár, Kristóf}, isbn = {978-3-99078-006-0}, issn = {2663-337X}, pages = {xviii+120}, publisher = {Institute of Science and Technology Austria}, title = {{Combinatorial width parameters for 3-dimensional manifolds}}, doi = {10.15479/AT:ISTA:8032}, year = {2020}, } @phdthesis{8983, abstract = {Metabolic adaptation is a critical feature of migrating cells. It tunes the metabolic programs of migrating cells to allow them to efficiently exert their crucial roles in development, inflammatory responses and tumor metastasis. Cell migration through physically challenging contexts requires energy. However, how the metabolic reprogramming that underlies in vivo cell invasion is controlled is still unanswered. In my PhD project, I identify a novel conserved metabolic shift in Drosophila melanogaster immune cells that by modulating their bioenergetic potential controls developmentally programmed tissue invasion. We show that this regulation requires a novel conserved nuclear protein, named Atossa. Atossa enhances the transcription of a set of proteins, including an RNA helicase Porthos and two metabolic enzymes, each of which increases the tissue invasion of leading Drosophila macrophages and can rescue the atossa mutant phenotype. Porthos selectively regulates the translational efficiency of a subset of mRNAs containing a 5’-UTR cis-regulatory TOP-like sequence. These 5’TOPL mRNA targets encode mitochondrial-related proteins, including subunits of mitochondrial oxidative phosphorylation (OXPHOS) components III and V and other metabolic-related proteins. Porthos powers up mitochondrial OXPHOS to engender a sufficient ATP supply, which is required for tissue invasion of leading macrophages. Atossa’s two vertebrate orthologs rescue the invasion defect. In my PhD project, I elucidate that Atossa displays a conserved developmental metabolic control to modulate metabolic capacities and the cellular energy state, through altered transcription and translation, to aid the tissue infiltration of leading cells into energy demanding barriers.}, author = {Emtenani, Shamsi}, issn = {2663-337X}, pages = {141}, publisher = {Institute of Science and Technology Austria}, title = {{Metabolic regulation of Drosophila macrophage tissue invasion}}, doi = {10.15479/AT:ISTA:8983}, year = {2020}, }